In bandpass modulators, a 2N-order loop filter can lead to an N-order noise shaping in the band of interest. This caused a bandpass modulator with more complex structure than a lowpass modulator and increased the power consumption and area of the modulator. In this paper, we proposed a discrete-time bandpass modulator using the noise-coupling technique that only needs to a second- order loop filter to have a second-order noise shaping. To realize a noise coupled bandpass modulator, we need to implement Z-2 delay block in the analog domain, but the proposed modulator uses only Z-1 delay blocks to apply the noise coupling technique. This simplifies the structure of the modulator and reduces the power consumption, area, and nonlinearity of the modulator. The error in the coupling path is considered and the effect of it on the modulator resolution is analyzed. According to the simulation results, the proposed modulator results in SNR = 84.9 dB at 80 MHz sampling frequency, 200 KHz bandwidth and OSR = 200.

In this paper, an efficient control algorithm for an Intelligent Controller Induction Motor Drive system using Fuzzy Logic Approach has been proposed. The Indirect Vector Control principle has been employed to control the Induction Motor. Next, a two-degree-of freedom controller is proposed to improve the system performance. The controller design algorithm can be applied in an adjustable speed control system to obtain good transient responses and good load disturbance rejection abilities. The proposed controller has been analyzed using computer simulation and compared with a simple conventional Controller strategy. The simulated controller performances have been finally verified experimentally using TMS320C6711 Digital Signal Processor. The results obtained substantiate the robustness and effectiveness of Intelligent Controller for high performance of Induction Motor.

In this research parallel Proportional-Derivative (PD) fuzzy logic theory plus Integral part (I) is used to compensate the system dynamic uncertainty controller according to highly nonlinear control theory sliding mode controller. Sliding mode controller (SMC) is an important considerable robust nonlinear controller. In presence of uncertainties, this controller is used to control of highly nonlinear systems especially for multi degrees of freedom (DOF) serial links robot manipulator. In opposition, sliding mode controller is an effective controller but chattering phenomenon and nonlinear equivalent dynamic formulation in uncertain dynamic parameters are two significant drawbacks. To reduce these challenges, new stable intelligent controller is introduce.

In this paper, a novel signal processing method is suggested for classifying epileptic seizures. To this end, first the Tangent and Hyperbolic Tangent of signals are calculated and then are classified into two classes: normal (or interictal) and ictal, using a proposed classifier. The results of this method show that the classification accuracy of normal and ictal classes (97.41%) has been higher than interictal and ictal classes (92.83%) and generally, it has a good potential to become a useful tool for physicians.

Any electromechanical device can be termed as Robot, which imitates human actions and in some of the situation can be used as a replacement for human. These days Robots are the integral part of our life and can be applied in several applications and tasks by giving respective commands. The research in robotics domain is to make it as autonomous and as much independent as it can be. The problem that arises is of controlling a mobile robot with the energy constraint. A lot of energy is wasted, if it takes wrong trajectory motion, this motion depends upon the robot knowledge which indeed in not constant. The variation in the environment results in making difficult for the robot to take precise and accurate measurements to reach the destination without much of the energy loss. An autonomous robot is expected to take decision according to the situation. For this precise decisions of robot path planning there are algorithms like A*, Dijkstra, D* etc. In this paper we have done analysis on partially known environment situation. Optimal path is planned by new heuristic approach over the A star algorithm, robot moving at an appropriate angle cuts down the unnecessary cost of path planning. Experimental results show that the proposed algorithm is much effective for more than 8% than the conventional A* algorithm in the same map environment.

The application of engineering practices in medicine has immensely contributed to the recent findings in biomedical research areas. One of the products of this application is the development of sophisticated aids for physically challenged people. In this paper, visually impaired walking aid is designed and implemented using a network of ultrasonic sensors, thereby capable of detecting the direction and position of obstacle(s). The performance and functionality are also improved by the addition of alert light, and voice guidance signal which is relayed to a miniature headset. The recorded voice alerts the user of the presence and direction of the obstacle(s). The prototype of the multidimensional walking aid was able to detect obstacles within the range of 0m to 1m at the left, right and front of the stick with an appropriate voice alert. The test results of the prototype showed that the stick can efficiently guide its user.

Bank plays the central role for the economic development world-wide. The failure and success of the banking sector depends upon the ability to proper evaluation of credit risk. Credit risk evaluation of any potential credit application has remained a challenge for banks all over the world till today. Artificial neural network plays a tremendous role in the field of finance for making critical, enigmatic and sensitive decisions those are sometimes impossible for human being. Like other critical decision in the finance, the decision of sanctioning loan to the customer is also an enigmatic problem. The objective of this paper is to design such a Neural Network that can facilitate loan officers to make correct decision for providing loan to the proper client. This paper checks the applicability of one of the new integrated model with nearest neighbor classifier on a sample data taken from a Bangladeshi Bank named Brac Bank. The Neural network will consider several factors of the client of the bank and make the loan officer informed about client’s eligibility of getting a loan. Several effective methods of neural network can be used for making this bank decision such as back propagation learning, regression model, gradient descent algorithm, nearest neighbor classifier etc.

The notion of rough set was introduced by Pawlak as an uncertainty based model, which basically depends upon single equivalence relations defined over a universe or a set of equivalence relations, which are not considered simultaneously. Hence, from the granular computing point of view it is unigranular by nature. Qian et al in 2006 and in 2010 introduced two types of multigranular rough sets (MGRS) called the optimistic and pessimistic MGRS respectively. The stringent notion of mathematical equality of sets was extended by introducing a kind of approximate equality, called rough equality by Novotny and Pawlak, which uses basic rough sets. Later three more related types of such approximate equalities have been introduced by Tripathy et al. He has also provided a comparative analysis of these four types of approximate equalities of sets leading to approximate reasoning in real life situations. Two of these four types of approximate equalities; namely the rough equality and rough equivalence have been extended to the context of multigranulations by Tripathy et al very recently. In this paper we carry out this study further by introducing the notion of approximate rough equalities for multigranulations and establish their properties. We use a real life example to illustrate the results in the paper and also to construct examples in support of some parts of the properties.

Performance Appraisal of employees plays a very critical role towards the growth of any organization. It has always been a tough task for any industry or organization as there is no unanimous scientific modus operandi for that. Performance Appraisal system is used to assess the capabilities and productiveness of the employees. In assessing employee performance, performance appraisal commonly includes assigning numerical values or linguistic labels to employees performance. However, the employee performance appraisal may include judgments which are based on imprecise data particularly when one employee tries to interpret another employee’s performance. Thus, the values assigned by the appraiser are only approximations and there is inherent vagueness in the evaluation. By fuzzy logic perspective, the performance of the appraisee includes the evaluation of his/her work ability, skills and adaptability which are absolutely fuzzy concepts that needs to be define in fuzzy terms. Hence, fuzzy approach can be used to examine these imprecise and uncertainty information. Consequently, the performance appraisal of employees can be accomplished by fuzzy logic approach and different defuzzification techniques are applied to rank the employees according to their performance, which shows inconsequential deviation in the rankings and hence proves the robustness of the system.

Spherical three Degree-of- Freedom (DOF) is controlled by model-base fuzzy computed torque controller. The spherical motor has three revolute joints allowing the corresponding parts to move horizontally and vertically. When developing a controller using conventional control methodology (e.g., feedback linearization methodology), a design scheme has to be produced, usually based on a system’s dynamic model. The work outline in this research utilizes soft computing applied to new conventional controller to address these methodology issues. Computed torque controller (CTC) is influential nonlinear controllers to certain systems which this method is based on compute the required arm torque using nonlinear feedback control law. When all dynamic and physical parameters are known, CTC works superbly; practically a large amount of systems have uncertainties and fuzzy feedback Inference Engine (FIS) is used to reduce this kind of limitation. Fuzzy logic provides functional capability without the use of a system dynamic model and has the characteristics suitable for capturing the approximate, varying values found in a MATLAB based area. Based on this research model- base fuzzy computed torque controller applied to spherical motor is presented to have a stable and robust nonlinear controller and have a good result compared with conventional and pure fuzzy logic controllers.